Every 15 Minutes

Introduction

In the summer of 2012, more than 100 maurauders on horseback crossed from Chad into Cameroon’s Bouba Ndjidah National park with one mission: to kill as many elephants as possible without getting caught. Using rocket-propelled grenades and AK-47s, they mowed down hundreds of elephants, decimating entire herds, all for one reason: ivory.

The illegal killing of elephants in Africa for their ivory takes places sometimes on a small scale, with poisoned watermelons, and other times on a large scale, using belt-fed automatic weapons. But regardless of the method, when an elephant comes into contact with a poacher, more often than not, the poacher gets what he came for. This is why between 2010 and 2012 alone, some 100,000 elephants were slaughtered. There only “crime” that they hold the ivory that the world cannot seem to resist.

This project seeks to understand the socio-economic factors that may contribute to poaching. It is crucial for us to understand these contributing factors so that they may be remedied, thus allowing for the continuation of elephants in the wild. For it has been estimated that, at the current rate of killing, all elephants in the wild will be gone within 10 years.

Several researchers have studied the problem, and their findings will be taken into account in the model. For instance, Lemieux and Clark (2009) studied the effect of regulated and unregulated markets and showed that the presence of unregulated markets, either in-country or in a bordering country, had a significant impact on poaching activity.

The initial dataset included socio-economic and governance data on 34 countries with elephant populations in Central, Western, Eastern, and Southern Africa. These data came from the International Monetary Fund (IMF), the UN Development Programme (UNDP), the Unesco Institute for Statistics, The World Bank, and Transparency International (see the codebook for specifics). Information regarding regulated and unregulated ivory markets in Africa came from Lemieux and Clark (2009), and conflict data came from the Uppsala Conflict Data Programme (UCDP). Data concerning elephant populations and poaching came from the Monitoring the Illegal Killing of Elephants (MIKE) program of CITES.

Together, these data will allow a comprehensive assessment of one side of the poaching equation, namely, conditions in Africa tat may contribute to illegal elephant killing. What these data do not address is the demand side of the equation. That is, we do not have the benefit of trafficking and sales data whereby illegal ivory is transmitted and sold to consumers, largely in Asian and Southeast Asian countries. Unfortunately such data is not readily accessible and gathering such data would be beyond the scope of this project.

1. Read in the necessary packages

# LOAD IN THE NECESSARY PACKAGES
library(ggplot2)
library(ggthemes)
library(GGally)
library(grDevices)
library(colorRamps)
library(dplyr)
library(RColorBrewer)
library(reporttools)
library(stargazer)
library(fpp)
library(xtable)
library(plotly)
library(Cairo)
library(MASS)
library(car)
library(Amelia)
library(corrplot)
library(caret)
options(xtable.floating = FALSE)
options(xtable.timestamp = "")

2. Data preprocessing

# READ IN DATASET
elephants <- read.csv("elephant_master_reduced.csv", header = TRUE)

sapply(elephants, function(x) sum(is.na(x)))

##                     country                        year 
##                           0                           0 
##                        ISO2                        ISO3 
##                          14                           0 
##                      region                 subregionid 
##                           0                           0 
##                     cap.lat                    cap.long 
##                           0                           0 
##                   NGDP_RPCH                       NGDPD 
##                           0                           0 
##                        PCPI                     PCPIPCH 
##                           0                           0 
##                    GGX_NGDP                 GGXWDG_NGDP 
##                           0                           0 
##                         BCA                         HDI 
##                           0                          21 
##                         GNI          Resource.Depletion 
##                          21                         191 
##              Adult.literacy       Primary.ed.enrollment 
##                          13                          42 
##              Mean.Schooling                   Total.pop 
##                         191                          21 
##         Pop.MultiDim.Povert       Deprivation.Intensity 
##                          38                          38 
##  Pop.Below.National.Poverty                 PPP.125.day 
##                          38                          38 
##       International.Dev.Aid Corruption.Perception.Index 
##                          21                          21 
##                  Reg.Market                Unreg.Market 
##                           0                           0 
##        Reg.Market.Bordering      Unreg.Market.Bordering 
##                           0                           0 
##        Voice.Accountability         Political.Stability 
##                          21                          21 
##    Government.Effectiveness                    Rule.Law 
##                          21                          21 
##          Corruption.Control                 Reg.Quality 
##                          21                          21 
##              Armed.Conflict          Non.State.Conflict 
##                           0                           0 
##   Non.State.Conflict.Deaths               PIKE.regional 
##                           0                          14 
##           Definite.Possible              Elephant.range 
##                         241                         241 
##               Tot.Carcasses           Illegal.Carcasses 
##                           0                           0 
##             Percent.Illegal 
##                           0

# CREATE VARIABLES TO ASSESS LEVEL OF ILLEGAL KILLING OF ELEPHANTS
elephants$High.Illegal <- factor(with(elephants, ifelse((Percent.Illegal > 0.5), 1, 0)))
table(elephants$High.Illegal)

## 
##   0   1 
## 161 140

There are several missing values in the dataset, so imputation will be necessary.

Note that the mean number of elephant carcasses for a given year is 49.32 and the mean number of illegal carcasses is 24.58, indicating that approximately half of all elephant deaths recorded are due to illegal poaching.

In conducting exploratory data analysis, we will focus on indicators by region. This is because there are significant differences in the Proportion of Illegally Killed Elephants (PIKE) for each region, so looking at patterns by region may provide some indication of factors that may be contributing.

3. Socio-economic indicators by region

p <- plot_ly(elephants, x = country, y = Resource.Depletion, 
             color = region, type = "box",
                width=640, height = 480) %>%
  layout(title = 'Depletion of Natural Resources (% of GNI)')
p

Resource depletion is a monetary expression of the level of energy, mineral, and forest depletion. We see from this plot that the countries of Central Africa have significantly higher resource depletion, on average, than the countries of other regions.

PCPI <- plot_ly(elephants, x = country, y = PCPI, color = region, type = "box",
                width=640, height = 480) %>%
  layout(title = 'Inflation, Average Consumer Prices')
PCPI

Inflation appears low, for the most part, in African countries. The exceptions are the Democratic Republic of Congo and Guinea.

GNI <- plot_ly(elephants, x = country, y = GNI, 
               color = region, type = "box",
                width=640, height = 480) %>%
  layout(title = 'Gross National Income')
GNI

With a few exceptions, the gross national income, which is the total of the domestic and foreign output of citizens of a country, appears to be highest in the countries of Southern Africa. At the same time, countries with lower GNI appear to have more stable GNIs.

International.Dev.Aid <- plot_ly(elephants, x = country, 
                                 y = International.Dev.Aid, color = region, 
                                 type = "box",
                width=640, height = 480) %>%
  layout(title = 'International Development Aid (in USD millions)')
International.Dev.Aid

pop.below.nat <- plot_ly(elephants, x = country, y = Pop.Below.National.Poverty, type = 'scatter', mode = 'markers', color = region, 
             marker = list(opacity = 0.8)) %>%
  layout(title = 'Percentage Below National Poverty Line')
pop.below.nat

The mean percentage of population below the national overty level is 0.46, and it appears that about two-thirds of the countries, particularly those in Western and Southern Africa, are above the mean.

pop.below.125 <- plot_ly(elephants, x = country, y = PPP.125.day, type = 'scatter', mode = 'markers', color = region, 
                         marker = list(opacity = 0.8)) %>%
  layout(title = 'Percentage Below National USD 1.25 per Day')
pop.below.125

The mean percentage of people living on less that USD 1.25 per day is 0.44. From the above graphic, it appears that about hallf fall above the mean and half below.

4. Governance indicators by region

Governance indicators reflect the degree to which government is responsive to its citizens. These indicators include voice accountability, indicating the degree to which citizens may participate in choosing their government; political stability, meaning an absence of violence; governement effectiveness, a reflection of the quality of public services; rule of law, the degree to which individuals have confidence in and abide by the rules of society; and corruption control, the extent to which public power is exercised for private gain.

elephantsFC <- filter(elephants, subregionid=="FC")
elephantsFW <- filter(elephants, subregionid=="FW")
elephantsFE <- filter(elephants, subregionid=="FE")
elephantsFS <- filter(elephants, subregionid=="FS")

governanceFC <- ggplot(data=elephantsFC, aes(x=year))+
  geom_line(aes(y=Voice.Accountability), color="cadetblue3")+
  geom_line(aes(y=Political.Stability), color="coral")+
  geom_line(aes(y=Government.Effectiveness), color="aquamarine")+
  geom_line(aes(y=Rule.Law), color="gold")+  
  geom_line(aes(y=Corruption.Control), color="palegreen")+
  geom_line(aes(y=Reg.Quality), color="darkorchid1")+
  ylim(-2, 3) + xlab(NULL) + ylab(NULL) +
  ggtitle("Governance Indicators for Central Africa") + theme_bw()
governanceFC + facet_wrap(~country, ncol = 4)

governanceFW <- ggplot(data=elephantsFW, aes(x=year))+
  geom_line(aes(y=Voice.Accountability), color="cadetblue3")+
  geom_line(aes(y=Political.Stability), color="coral")+
  geom_line(aes(y=Government.Effectiveness), color="aquamarine")+
  geom_line(aes(y=Rule.Law), color="gold")+
  geom_line(aes(y=Corruption.Control), color="palegreen")+
  geom_line(aes(y=Reg.Quality), color="darkorchid1")+
  ylim(-2, 3) + xlab(NULL) + ylab(NULL) +
  ggtitle("Governance Indicators for Western Africa")+ theme_bw()
governanceFW + facet_wrap(~country, ncol = 4)

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

governanceFE <- ggplot(data=elephantsFE, aes(x=year))+
  geom_line(aes(y=Voice.Accountability), color="cadetblue3")+
  geom_line(aes(y=Political.Stability), color="coral")+
  geom_line(aes(y=Government.Effectiveness), color="aquamarine")+
  geom_line(aes(y=Rule.Law), color="gold")+
  geom_line(aes(y=Corruption.Control), color="palegreen")+
  geom_line(aes(y=Reg.Quality), color="darkorchid1")+
  ylim(-2, 3) + xlab(NULL) + ylab(NULL) +
  ggtitle("Governance Indicators for Eastern Africa")+ theme_bw()
governanceFE + facet_wrap(~country, ncol = 4)

governanceFS <- ggplot(data=elephantsFS, aes(x=year))+
  geom_line(aes(y=Voice.Accountability), color="cadetblue3")+
  geom_line(aes(y=Political.Stability), color="coral")+
  geom_line(aes(y=Government.Effectiveness), color="aquamarine")+
  geom_line(aes(y=Rule.Law), color="gold")+
  geom_line(aes(y=Corruption.Control), color="palegreen")+
  geom_line(aes(y=Reg.Quality), color="darkorchid1")+
  ylim(-2, 3) + xlab(NULL) + ylab(NULL) +
  ggtitle("Governance Indicators for Southern Africa")+ theme_bw()
governanceFS + facet_wrap(~country, ncol = 4)

From these visualizations we can see that governance indicators show a great deal of volatility, especially with respect to the countries of Central Africa and several of those in Southern Africa.

5. Normalize/Standardize the data

# NORMALIZE AND STANDARDIZE THE DATA
centered.elephants <- data.frame(scale(elephants[,c(9:28,33:38,41:47)]))
centered.elephants <- cbind(elephants[,c(1:8, 29:32,39:40, 48)], centered.elephants)
centered.elephants$High.Illegal <- as.integer(centered.elephants$High.Illegal)

summary(centered.elephants)

##          country         year           ISO2          ISO3    
##  Kenya       : 14   Min.   :2002   KE     : 14   KEN    : 14  
##  Namibia     : 14   1st Qu.:2005   UG     : 14   NAM    : 14  
##  South Africa: 14   Median :2009   ZA     : 14   UGA    : 14  
##  Uganda      : 14   Mean   :2009   ZM     : 14   ZAF    : 14  
##  Zambia      : 14   3rd Qu.:2012   ZW     : 14   ZMB    : 14  
##  Zimbabwe    : 14   Max.   :2015   (Other):217   ZWE    : 14  
##  (Other)     :217                  NA's   : 14   (Other):217  
##              region   subregionid    cap.lat           cap.long     
##  Central Africa :73   FC:73       Min.   :-25.580   Min.   :-17.29  
##  Eastern Africa :62   FE:62       1st Qu.:-15.280   1st Qu.:  7.32  
##  Southern Africa:83   FS:83       Median :  0.200   Median : 17.04  
##  Western Africa :83   FW:83       Mean   : -2.353   Mean   : 17.45  
##                                   3rd Qu.:  9.020   3rd Qu.: 31.02  
##                                   Max.   : 15.190   Max.   : 38.55  
##                                                                     
##    Reg.Market      Unreg.Market   Reg.Market.Bordering
##  Min.   :0.0000   Min.   :0.000   Min.   :0.0000      
##  1st Qu.:0.0000   1st Qu.:0.000   1st Qu.:0.0000      
##  Median :0.0000   Median :0.000   Median :0.0000      
##  Mean   :0.1827   Mean   :0.196   Mean   :0.6213      
##  3rd Qu.:0.0000   3rd Qu.:0.000   3rd Qu.:0.0000      
##  Max.   :1.0000   Max.   :1.000   Max.   :3.0000      
##                                                       
##  Unreg.Market.Bordering Armed.Conflict  Non.State.Conflict  High.Illegal  
##  Min.   :0.000          Min.   :0.000   Min.   :0.0000     Min.   :1.000  
##  1st Qu.:1.000          1st Qu.:0.000   1st Qu.:0.0000     1st Qu.:1.000  
##  Median :2.000          Median :0.000   Median :0.0000     Median :1.000  
##  Mean   :1.625          Mean   :0.186   Mean   :0.1561     Mean   :1.465  
##  3rd Qu.:2.000          3rd Qu.:0.000   3rd Qu.:0.0000     3rd Qu.:2.000  
##  Max.   :4.000          Max.   :1.000   Max.   :1.0000     Max.   :2.000  
##                                                                           
##    NGDP_RPCH           NGDPD              PCPI            PCPIPCH       
##  Min.   :-8.5293   Min.   :-0.3618   Min.   :-0.5352   Min.   :-5.8036  
##  1st Qu.:-0.3300   1st Qu.:-0.3096   1st Qu.:-0.3403   1st Qu.:-0.3793  
##  Median : 0.1156   Median :-0.2634   Median :-0.2831   Median :-0.1315  
##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000  
##  3rd Qu.: 0.4595   3rd Qu.:-0.1620   3rd Qu.:-0.1089   3rd Qu.: 0.2270  
##  Max.   : 6.0386   Max.   :10.4778   Max.   : 6.2945   Max.   :10.8290  
##                                                                         
##     GGX_NGDP        GGXWDG_NGDP           BCA                HDI         
##  Min.   :-2.3099   Min.   :-0.9185   Min.   :-4.54036   Min.   :-1.9909  
##  1st Qu.:-0.6149   1st Qu.:-0.5280   1st Qu.:-0.11323   1st Qu.:-0.7003  
##  Median :-0.1572   Median :-0.2934   Median : 0.06001   Median :-0.1235  
##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.0000  
##  3rd Qu.: 0.4249   3rd Qu.: 0.1494   3rd Qu.: 0.16136   3rd Qu.: 0.4949  
##  Max.   : 5.1196   Max.   : 9.5444   Max.   : 8.32849   Max.   : 2.3208  
##                                                         NA's   :21       
##       GNI           Resource.Depletion Adult.literacy   
##  Min.   :-0.78873   Min.   :-0.78585   Min.   :-1.8109  
##  1st Qu.:-0.58528   1st Qu.:-0.60130   1st Qu.:-0.4468  
##  Median :-0.40793   Median :-0.35802   Median : 0.1428  
##  Mean   : 0.00000   Mean   : 0.00000   Mean   : 0.0000  
##  3rd Qu.:-0.01034   3rd Qu.: 0.04883   3rd Qu.: 0.9773  
##  Max.   : 3.30277   Max.   : 3.59869   Max.   : 1.3620  
##  NA's   :21         NA's   :191        NA's   :13       
##  Primary.ed.enrollment Mean.Schooling      Total.pop      
##  Min.   :-2.2945       Min.   :-1.6722   Min.   :-0.7589  
##  1st Qu.:-0.6960       1st Qu.:-0.8276   1st Qu.:-0.6433  
##  Median : 0.4682       Median : 0.1059   Median :-0.3322  
##  Mean   : 0.0000       Mean   : 0.0000   Mean   : 0.0000  
##  3rd Qu.: 0.7722       3rd Qu.: 0.5060   3rd Qu.: 0.1736  
##  Max.   : 1.2227       Max.   : 2.1062   Max.   : 5.1226  
##  NA's   :42            NA's   :191       NA's   :21       
##  Pop.MultiDim.Povert Deprivation.Intensity Pop.Below.National.Poverty
##  Min.   :-0.77386    Min.   :-1.8583       Min.   :-1.88540          
##  1st Qu.:-0.57802    1st Qu.:-0.8221       1st Qu.:-0.68390          
##  Median :-0.36804    Median :-0.1059       Median : 0.05715          
##  Mean   : 0.00000    Mean   : 0.0000       Mean   : 0.00000          
##  3rd Qu.:-0.02326    3rd Qu.: 0.5798       3rd Qu.: 0.66509          
##  Max.   : 3.59793    Max.   : 2.1646       Max.   : 1.91336          
##  NA's   :38          NA's   :38            NA's   :38                
##   PPP.125.day       International.Dev.Aid Corruption.Perception.Index
##  Min.   :-1.89838   Min.   :-0.7880       Min.   :-1.4858            
##  1st Qu.:-0.57562   1st Qu.:-0.6316       1st Qu.:-0.6962            
##  Median :-0.04553   Median :-0.2990       Median :-0.3014            
##  Mean   : 0.00000   Mean   : 0.0000       Mean   : 0.0000            
##  3rd Qu.: 0.80659   3rd Qu.: 0.3669       3rd Qu.: 0.2908            
##  Max.   : 2.14421   Max.   : 7.3977       Max.   : 3.1531            
##  NA's   :38         NA's   :21            NA's   :21                 
##  Voice.Accountability Political.Stability Government.Effectiveness
##  Min.   :-2.2123      Min.   :-2.3099     Min.   :-1.94399        
##  1st Qu.:-0.8154      1st Qu.:-0.7518     1st Qu.:-0.82597        
##  Median : 0.1171      Median : 0.0425     Median :-0.01403        
##  Mean   : 0.0000      Mean   : 0.0000     Mean   : 0.00000        
##  3rd Qu.: 0.6751      3rd Qu.: 0.8174     3rd Qu.: 0.38557        
##  Max.   : 1.7948      Max.   : 1.9631     Max.   : 2.42605        
##  NA's   :21           NA's   :21          NA's   :21              
##     Rule.Law       Corruption.Control  Reg.Quality     
##  Min.   :-1.8022   Min.   :-1.4243    Min.   :-2.3549  
##  1st Qu.:-0.8068   1st Qu.:-0.7361    1st Qu.:-0.7376  
##  Median : 0.1324   Median :-0.2157    Median : 0.1326  
##  Mean   : 0.0000   Mean   : 0.0000    Mean   : 0.0000  
##  3rd Qu.: 0.6060   3rd Qu.: 0.4095    3rd Qu.: 0.6048  
##  Max.   : 2.2276   Max.   : 3.1580    Max.   : 1.9437  
##  NA's   :21        NA's   :21         NA's   :21       
##  Non.State.Conflict.Deaths PIKE.regional     Definite.Possible 
##  Min.   :-0.234            Min.   :-1.6331   Min.   :-0.53052  
##  1st Qu.:-0.234            1st Qu.:-0.7531   1st Qu.:-0.51666  
##  Median :-0.234            Median :-0.2788   Median :-0.42619  
##  Mean   : 0.000            Mean   : 0.0000   Mean   : 0.00000  
##  3rd Qu.:-0.234            3rd Qu.: 1.0344   3rd Qu.:-0.01505  
##  Max.   : 9.719            Max.   : 1.7745   Max.   : 3.91046  
##                            NA's   :14        NA's   :241       
##  Elephant.range    Tot.Carcasses      Illegal.Carcasses 
##  Min.   :-0.8947   Min.   :-0.53091   Min.   :-0.48580  
##  1st Qu.:-0.7287   1st Qu.:-0.48785   1st Qu.:-0.46604  
##  Median :-0.5119   Median :-0.36944   Median :-0.38700  
##  Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.00000  
##  3rd Qu.: 0.4491   3rd Qu.:-0.03573   3rd Qu.:-0.05108  
##  Max.   : 3.0532   Max.   : 7.07978   Max.   : 8.38660  
##  NA's   :241                                            
##  Percent.Illegal   
##  Min.   :-1.33852  
##  1st Qu.:-1.00876  
##  Median : 0.06352  
##  Mean   : 0.00000  
##  3rd Qu.: 0.90474  
##  Max.   : 1.46556  
## 

Several of the variables still appear to be highly skewed, so further transformation will be required, as the Amelia II package imputes missing values assuming a normal distribution.

6. Check for correlated variables

# EXAMINE DATA FOR HIGHLY CORRELATED VARIABLES
clrs <- brewer.pal(10, "Spectral")
cors <- cor(centered.elephants[,9:47], use = "pairwise")
corrplot <- corrplot.mixed(cors, col = clrs, number.cex=0.3,
                     tl.pos="lt", tl.cex=0.5, tl.col="black",
                     tl.srt=45)

# REMOVE HIGHLY CORRELATED VARIABLES
pd.elephants <- subset(centered.elephants, 
                       subset=TRUE, select=c(1:22,24:28,30:34,36:47))

As we can see, there are a significant number of highly correlated variables. Linear regression was run on each of them with illegal carcasses as the dependent variable to determine which of the correlated variables should be removed. When two variables were compared, the variable with the highest \[R^2\] was retained. Three variables were deleted from the dataset including Human Development Index, Total Population, and Corruption Perception Index. We also see that of the observed values of total elephant carasses in relation to illegal carcasses, 252 observations meet the condition of more than 50% of the total were killed illegally.

7. Create training and testing datasets

# SEPARATE DATA INTO TRAINING AND TESTING SETS
inVal <- createDataPartition(pd.elephants$High.Illegal, p = 0.6, list=FALSE)
eleph.train <- pd.elephants[inVal,]
eleph.test <- pd.elephants[-inVal,]

Because there is a low number of observations relative to the number of variables, the training dataset threshold was determined to be 60%.

8. Impute missing values to training and testing sets

# IMPUTE MISSING VALUES
library(snow)
set.seed(1357)
elephants.out.train <- amelia(eleph.train, m=1, frontend = FALSE, 
                              idvars = c("ISO2", "ISO3", "region", 
                                         "subregionid", "cap.lat", "cap.long"),
                              ts = "year", cs = "country", 
                              polytime = 0, intercs = TRUE, p2s = 1, 
                              parallel="snow", ncpus = 3, 
                             empri = .01*nrow(eleph.train))

elephants.out.test <- amelia(eleph.test, m=1, frontend = FALSE, 
                              idvars = c("ISO2", "ISO3", "region", 
                                         "subregionid", "cap.lat", "cap.long"),
                              ts = "year", cs = "country", 
                              intercs = TRUE, p2s = 1, 
                              parallel="snow", ncpus = 3, 
                             empri = .015*nrow(eleph.test))

## Amelia Error Code:  34 
##  The number of observations in too low to estimate the number of 
##  parameters.  You can either remove some variables, reduce 
##  the order of the time polynomial, or increase the empirical prior.

# Amelia II: Multiple Imputation
# (Version 1.7.4, built: 2015-12-05)
# Copyright (C) 2005-2017 James Honaker, Gary King and Matthew Blackwell
# Refer to http://gking.harvard.edu/amelia/ for more information

9. Spot check variables with high missingness

Running the tscs plots will allow us to determine whether the imputation of missing values seems reasonable.

# SPOT CHECK VARIABLES WITH HIGH MISSINGNESS
par(mfrow=c(3,2))
tscsPlot(elephants.out.train, cs = "Kenya", main = "Kenya",
          var = "Resource.Depletion", ylim = c(-2,0))
tscsPlot(elephants.out.train, cs = "Botswana", main = "Botswana",
         var = "Resource.Depletion", ylim = c(-2,0))
tscsPlot(elephants.out.train, cs = "Cameroon", main = "Cameroon",
         var = "Definite.Possible", ylim = c(-2,0))
tscsPlot(elephants.out.train, cs = "Chad", main = "Chad",
         var = "Definite.Possible", ylim = c(-2,0))
tscsPlot(elephants.out.train, cs = "Zimbabwe", main = "zimbabwe",
         var = "Primary.ed.enrollment", ylim = c(-2,2))
tscsPlot(elephants.out.train, cs = "Congo Republic", main = "Congo Republic",
         var = "Primary.ed.enrollment", ylim = c(-2,2))

The plots show that, although there is some variation to the imputation, overall the values imputed seem reasonable. From here, we will begin modeling the data in order to determine those factors that may contribute to elephant poaching.